Relaxed matching kernels for robust image comparison

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Vedaldi, Andrea ^a   Soatto, Stefano ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; FEATURE EXTRACTION; IMAGE PROCESSING; KETONES; PATTERN RECOGNITION; STANDARDS;

DATA-SETS; IMAGE COMPARISON; IMAGE PATCHES; MATCHING GRAPHS; SPATIAL INFORMATIONS;

OBJECT RECOGNITION;

EID: 51949101807     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587619     Document Type: Conference Paper

References (25)

1. F. L. Bookstein. Principal warps: Thin-plate splines and the decomposition of deformations. PAMI, 11(6):567-585, 1989.
2. G. Csurka, C. R. Dance, L. Dan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In Proc. ECCV, 2004.
3. G. Dorkó. Scale and affine invariant local detectors and descriptors. Technical report, INRIA, 2005.
4. L. Fei-Fei, R. Fergus, and P. Perona. A Bayesian approach to unsupervised one-shot learning of object categories. In Proc. ICCV, 2003.
5. P. F. Felzenszwalb and D. P. Huttenlocher. Pictorial structures for object recognition. IJCV, 61(1), 2005.
6. R. Fergus, P. Perona, and A. Zisserman. A sparse object category model for efficient learning and exhaustive recognition. In Proc. CVPR, 2005.
7. K. Grauman and T. Darrell. Pyramid match kernels: Discriminative calssification with sets of image features. Technical Report MIT-CSAIL-TR-2006-020, MIT, 2006.
8. C. Guo, S.-C. Zhu, and Y. N. Wu. Towards a mathematical theory of primal sketch and sketchability. In Proc. ICCV, page 1228, 2003.
9. M. Hein and O. Bousquet. Hilbertian metrics and positive definite kernels on probability measures. In Proc. AISTAT, 2005.
10. H. Kashima and A. Inokuchi. Kernels for graph classification. In ICDM Workshop on Active Mining, 2002.
11. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bag of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, 2006.
12. L. Lin, S.-C. Zhu, and Y. Wang. Layered graph matching with graph editing. In Proc. CVPR, 2007.
13. H. Ling and S. Soatto. Proximity distribution kernels for geometric context in category recognition. In Proc. CVPR, 2007.
14. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2(60):91-110, 2004.
15. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool. A comparison of affine region detectors. IJCV, 1(60):63-86, 2004.
16. E. Nowak, F. Jurie, and B. Triggs. Sampling strategies for bag-of-features image classification. In Proc. ECCV, 2006.
17. F. Perronnin and C. Dance. Fisher kenrels on visual vocabularies for image categorizaton. In Proc. CVPR, 2006.
18. H. Qiu and E. R. Hancock. Graph matching and clustering using spectral partitions. Pattern Recognition, 39, 2006.
19. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2002.
20. N. Slonim and N. Tishby. Agglomerative information bottleneck. In Proc. NIPS, 1999.
21. T. Tuytelaars and C. Schmid. Vector quantizing feature space with a regular lattice. In Proc. ICCV, 2007.
22. A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://vision.ucla.edu/vlfeat, 2008.
23. A. Vedaldi and S. Soatto. Features for recognition: Viewpoint invariance for non-planar scenes. In Proc. ICCV, 2005.
24. M. Weber, M. Welling, and P. Perona. Unsupervised learning of models for recognition. In Proc. ECCV, 2000.
25. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: A comprehensive study. IJCV, 2006.